Optimal filter-based detection of microcalcifications

Deals with the problem of texture feature extraction in digital mammograms. The authors use the extracted features to discriminate between texture representing clusters of microcalcifications and texture representing normal tissue. Having a two-class problem, the authors suggest a texture feature extraction method based on a single filter optimized with respect to the Fisher criterion. The advantage of this criterion is that it uses both the feature mean and the feature variance to achieve good feature separation. Image compression is desirable to facilitate electronic transmission and storage of digitized mammograms. In this paper, the authors also explore the effects of data compression on the performance of their proposed detection scheme. The mammograms in their test set were compressed at different ratios using the Joint Photographic Experts Group compression method. Results from an experimental study indicate that the authors' scheme is very well suited for detecting clustered microcalcifications in both uncompressed and compressed mammograms. For the uncompressed mammograms, at a rate of 1.5 false positive clusters/image the authors' method reaches a true positive rate of about 95%, which is comparable to the best results achieved so far. The detection performance for images compressed by a factor of about four is very similar to the performance for uncompressed images.